Are vegetation indices useful in the Arctic?

AbstractThis paper describes a preliminary investigation of the extent to which the normalised difference vegetation index (NDVI), derived from satellite optical imagery, can indicate the extent of damage to upland tundra (fruticose lichen and dwarf shrub) vegetation. We combine the results of a previously reported classification of Landsat multispectral scanner imagery from Kol'skiy Poluostrov, Russia, with field measurements of the biomass and spectral reflectance of tundra vegetation. The results show that the NDVI is not strongly influenced by biomass, but that differences in species composition and ground cover are significant. Other workers have concluded that vegetation indices are not useful for boreal forests. It is therefore suggested that the use of the NDVI by itself as an indicator of the state of disturbed vegetation in Arctic regions is not recommended.

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Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

Italian Journal of Agronomy ◽

10.4081/ija.2015.647 ◽

2015 ◽

Vol 10 (4) ◽

pp. 215 ◽

Cited By ~ 2

Author(s):

Roberta Rossi ◽

Alessio Pollice ◽

Gianfranco Bitella ◽

Rocco Bochicchio ◽

Amedeo D'Antonio ◽

...

Keyword(s):

Electrical Resistivity ◽

Medicago Sativa ◽

Vegetation Index ◽

Nonlinear Models ◽

Ground Cover ◽

Plant Productivity ◽

Additive Models ◽

Deep Soil ◽

Soil Features ◽

Normalised Difference Vegetation Index

Alfalfa is a highly productive and fertility-building forage crop; its performance, can be highly variable as influenced by within-field soil spatial variability. Characterising the relations between soil and forage- variation is important for optimal management. The aim of this work was to model the relationship between soil electrical resistivity (ER) and plant productivity in an alfalfa (Medicago sativa L.) field in Southern Italy. ER mapping was accomplished by a multi-depth automatic resistivity profiler. Plant productivity was assessed through normalised difference vegetation index (NDVI) at 2 dates. A non-linear relationship between NDVI and deep soil ER was modelled within the framework of generalised additive models. The best model explained 70% of the total variability. Soil profiles at six locations selected along a gradient of ER showed differences related to texture (ranging from clay to sandy-clay loam), gravel content (0 to 55%) and to the presence of a petrocalcic horizon. Our results prove that multi-depth ER can be used to localise permanent soil features that drive plant productivity.

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Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

Revista de Teledetección ◽

10.4995/raet.2018.8891 ◽

2018 ◽

pp. 99 ◽

Cited By ~ 1

Author(s):

V. Egea-Cobrero ◽

V. Rodriguez-Galiano ◽

E. Sánchez-Rodríguez ◽

M.A. García-Pérez

Keyword(s):

Vegetation Index ◽

Net Primary Production ◽

Vegetation Indices ◽

Yield Data ◽

Imaging Spectrometer ◽

Wheat Production ◽

Production Studies ◽

Normalised Difference Vegetation Index ◽

Chlorophyll Index ◽

The One

There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP—Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC—Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R2 =0.81 and R2 =0.57, respectively).

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Estimating Grassland Curing with Remotely Sensed Data

10.5194/nhess-2017-101 ◽

2017 ◽

Author(s):

Wasin Chaivaranont ◽

Jason P. Evans ◽

Yi Y. Liu ◽

Jason J. Sharples

Keyword(s):

Vegetation Index ◽

Field Measurements ◽

Fire Danger ◽

Remotely Sensed Data ◽

Vegetation Water Content ◽

Grassland Ecosystems ◽

Normalised Difference Vegetation Index ◽

Dead Material ◽

Degree Of Curing ◽

Vegetation Water

Abstract. Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire 10 Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, therefore, ground observed measurements are rather limited. In this study, we used satellite observed vegetation greenness (Normalised Difference Vegetation Index, NDVI) and vegetation water content (Vegetation Optical Depth, VOD) information to improve the accuracy of the DOC estimation. First, a statistically 15 significant relationship is established between selected ground observed DOC and satellite observed vegetation datasets (NDVI and VOD) with an r2 of 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.55. Results suggest that satellite based DOC estimation can reasonably reproduce ground based observations in space and time. Comparison with currently available satellite based DOC products shows that our model has a comparable and arguably more balanced performance.

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Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard

Remote Sensing ◽

10.3390/rs12244144 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4144

Author(s):

José Luis Gallardo-Salazar ◽

Marín Pompa-García

Keyword(s):

Unmanned Aerial Vehicles ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Field Measurements ◽

Tree Level ◽

Individual Tree ◽

Basal Diameter ◽

Crown Diameter ◽

Aerial Vehicles

Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology to extract tree-level characteristics using UAVs in a spatially distributed area of pine trees on a regular basis. Analysis included different vegetation indices estimated with a high-resolution orthomosaic. Statistically reliable results were found through a three-phase workflow consisting of image acquisition, canopy analysis, and validation with field measurements. Of the 117 trees in the field, 112 (95%) were detected by the algorithm, while height, area, and crown diameter were underestimated by 1.78 m, 7.58 m2, and 1.21 m, respectively. Individual tree attributes obtained from the UAV, such as total height (H) and the crown diameter (CD), made it possible to generate good allometric equations to infer the basal diameter (BD) and diameter at breast height (DBH), with R2 of 0.76 and 0.79, respectively. Multispectral indices were useful as tree vigor parameters, although the normalized-difference vegetation index (NDVI) was highlighted as the best proxy to monitor the phytosanitary condition of the orchard. Spatial variation in individual tree productivity suggests the differential management of ramets. The consistency of the results allows for its application in the field, including the complementation of spectral information that can be generated; the increase in accuracy and efficiency poses a path to modern inventories. However, the limitation for its application in forests of more complex structures is identified; therefore, further research is recommended.

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Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

Remote Sensing ◽

10.3390/rs11070799 ◽

2019 ◽

Vol 11 (7) ◽

pp. 799 ◽

Cited By ~ 4

Author(s):

Rachel Lugassi ◽

Eli Zaady ◽

Naftaly Goldshleger ◽

Maxim Shoshany ◽

Alexandra Chudnovsky

Keyword(s):

Crude Protein ◽

Vegetation Index ◽

Vegetation Indices ◽

Field Measurements ◽

Neutral Detergent Fiber ◽

Ecological Quality ◽

Spatial And Temporal Patterns ◽

Validation Data ◽

Calibration And Validation ◽

Sentinel 2

Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.

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Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

Biogeosciences ◽

10.5194/bg-10-6279-2013 ◽

2013 ◽

Vol 10 (10) ◽

pp. 6279-6307 ◽

Cited By ~ 29

Author(s):

E. Boegh ◽

R. Houborg ◽

J. Bienkowski ◽

C. F. Braban ◽

T. Dalgaard ◽

...

Keyword(s):

Remote Sensing ◽

Field Data ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Field Measurements ◽

Leaf Nitrogen ◽

Agricultural Landscapes ◽

Nitrogen Pools ◽

Spectral Vegetation Indices

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.

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Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

Remote Sensing ◽

10.3390/rs13214466 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4466

Author(s):

Isabell Eischeid ◽

Eeva M. Soininen ◽

Jakob J. Assmann ◽

Rolf A. Ims ◽

Jesper Madsen ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Predictive Ability ◽

Ground Cover ◽

Arctic Tundra ◽

The Arctic ◽

Ecosystem Monitoring ◽

Selection Of Variables ◽

State Changes ◽

Study Sites

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs.

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Fire-induced changes in soil and vegetation in the forest-tundra of Western Siberia

E3S Web of Conferences ◽

10.1051/e3sconf/202022303001 ◽

2020 ◽

Vol 223 ◽

pp. 03001

Author(s):

Oleg Sizov ◽

Leya Brodt ◽

Andrey Soromotin ◽

Nikolay Prikhodko ◽

Ramona Heim

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Western Siberia ◽

The Arctic ◽

Fire Scars ◽

Forest Tundra ◽

The North ◽

Tundra Vegetation ◽

Lichen Cover ◽

The Face

Wildfires are one of the main factors for landscape change in tundra ecosystems. In the absence of external mechanical impacts, tundra plant communities are relatively stable, even in the face of climatic changes. In our study, lichen cover was degraded on burnt tundra sites, which increased the permafrost thaw depth from 100 to 190 cm. In old fire scars (burnt 1980 – 1990) of the forest-tundra, vegetation cover was dominated by trees and shrubs. The soil temperature on burnt forest-tundra sites was higher in comparison to conditions of the unburnt control sites and permafrost was was not found at a depth of 2-2,3m. Dynamics of the Normalized Difference Vegetation index (NDVI) from 1986-2020 reveal that immediately after fires, vegetation recovered and biomass increased due to the development of Betula nana shrubs. In old fire scars of the forest-tundra (burnt 1980-1990), a significant increase in NDVI values was evident, in contrast to the unburnt tundra vegetation where this trend was less pronounced. We conclude that "greening" in the north of Western Siberia may occur due to fire-induced transformation processes. The role of wildfires in the advance of the treeline to the north, driven by climate change and active economic development of the Arctic, will gradually increase in future.

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Mapping of Sub-Optimal Land Based on NDVI Sentinel 2a Value: Preliminary Study

Jurnal Presipitasi Media Komunikasi dan Pengembangan Teknik Lingkungan ◽

10.14710/presipitasi.v17i3.194-204 ◽

2020 ◽

Vol 17 (3) ◽

pp. 194-204

Author(s):

Indarto Indarto ◽

Rufiani Nadzirah ◽

Hadrian Reksa Belagama

Keyword(s):

Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Vegetation Density ◽

Residential Areas ◽

Primary Input ◽

Normalised Difference Vegetation Index ◽

Sentinel 2A ◽

Marginal Agricultural Land ◽

Potential Use

Normalised Difference Vegetation Index (NDVI) is one of the vegetation indices used to analyse vegetation density. This study presents the potential use of NDVI to map dry-marginal-agricultural land (Dry-MAL). The study conducted in the eastern part of Situbondo, which includes three districts, namely, Arjasa, Asembagus and Jangkar. Sentinel-2A (recorded in 2018) and 450 Control points (GCPs) are used as the primary input. The region is an area with distinctive climate characteristics, where the dry season is longer than the rainy season. Analysis using "SNAP plug-ins" and "QGIS". Research procedures include (1) data inventory, (2) data pre-processing, (3) data processing and (4) accuracy testing. The NDVI classification can distinguish six (6) classes of land-use, i.e., water bodies, residential areas, dry MAL, non-irrigated rural area, irrigated paddy fields, forest-plantations. The NDVI classification produces Overall and Kappa accuracy values = 66,9% and 61,6%. Although the overall and kappa accuracy is below the standard, however, the result will benefit for further research of index vegetation or soil more applied for the identification of Dry-MAL

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Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

Remote Sensing ◽

10.3390/rs12182970 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2970

Author(s):

Anna C. Talucci ◽

Elena Forbath ◽

Heather Kropp ◽

Heather D. Alexander ◽

Jennie DeMarco ◽

...

Keyword(s):

Boreal Forests ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Visible Spectrum ◽

Basal Area ◽

Vegetation Recovery ◽

Climate Conditions ◽

Northeastern Siberia ◽

Larch Forests

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.

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